Aspect-Oriented Incremental Customization of Middleware Services
Bibliographic record
Abstract
As distributed applications evolve, incremental customization of middleware services is often required; these customizations should be unpluggable, modular, and efficient. This is difficult to achieve because the customizations depend on both application-specific needs and the services provided. Although middleware allows programmers to separate application-specific functionality from lower-level details, traditional methods of customization do not allow efficient modularization. Currently, making even minor changes to customize middleware is complicated by the lack of locality. Programmers may have to compromise between the two extremes: to interpose a simple, well-localized layer of functionality between the application and middleware, or to make a large number of small, poorly localized, invasive changes to all execution points which interact with middleware services. Although the invasive approach allows a more efficient customization, it is harder to ensure consistency, more tedious to implement, and exceedingly difficult to unplug. Thus, a common approach is to add an extra layer for systemic concerns such as robustness, caching, filtering, and security. Aspect-oriented programming (AOP) offers a potential alternative between the interposition and invasive approaches by providing modular support for the implementation of crosscutting concerns. AOP enables the implementation of efficient customizations in a structured and unpluggable manner. We demonstrate this approach by comparing traditional and AOP customizations of fault tolerance in a distributed file system model, JNFS. Our results show that using AOP can reduce the amount of invasive code to almost zero, improve efficiency by leveraging the existing application behaviour, and facilitate incremental customization and extension of middleware services.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".